Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation using Naturalistic Driving Data and Augmented Reality

Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation using Naturalistic Driving Data and Augmented Reality

Yiheng Feng Headshot - link directs to their research page
Yiheng Feng
Shan Bao Headshot - link directs to their research page
Shan Bao
Headshot of CCAT Director, Henry Liu - link directs to their research page
Henry Liu
UMTRI Logo - link directs to U-M research page

Principal Investigator(s):

Yiheng Feng, Assistant Research Scientist – The University of Michigan Transportation Research Institute
Shan Bao, Associate Research Scientist – The University of Michigan Transportation Research Institute
Associate Professor of Industrial and Manufacturing Systems Engineering – University of Michigan-Dearborn
Henry Liu, Director – Center for Connected and Automated Transportation (CCAT)
Professor of Civil and Environmental Engineering – The University of Michigan
Research Professor – The University of Michigan Transportation Research Institute

Project Abstract:
Testing and evaluation is a critical step in development and deployment of connected and automated vehicle (CAV) technology. Testing standards for human-driven vehicles, such as Federal Motor Vehicle Safety Standards (FMVSS), have been established a long time ago. However, current standards cannot be applied to CAVs, because they often assume the presence of a human driver, who conducts the driving tasks. It is very important to develop test procedures and identify applicable testing scenarios (user cases) for CAVS to evaluate the “intelligence” of the vehicle. The intelligence level indicates whether a CAV can drive safely and efficiently without human intervention. The newly released Automated Driving Systems Guideline 2 has made it very clear that the new automated driving systems needs validation methods and needs to be tested by incorporating behavior competencies. In this project, we will investigate how to design such testing scenario libraries by looking into crash and naturalistic driving databases, and how to implement the defined scenarios in the augmented reality (AR) testing environment. We focus on testing higher levels of automation defined by SAE (level 3 or higher), in which human behaviors are much less involved in the driving tasks. A general framework work will be proposed to generate testing scenarios and with theoretical foundations. Several representative testing scenarios will be identified and implemented in the augmented reality (AR) testing environment. The identified testing scenarios will first be constructed in the simulation platform with realistic driver behaviors calibrated from naturalistic driving data (NDD). A real CAV will be tested under the scenarios and its performance will be recorded and evaluated in terms of accuracy and efficiency.

Research Thrust(s): Control & Operations, Enabling TechnologyHuman FactorsModeling & Implementation

Project Form(s):
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